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Reseach Article

Optimizing Back-Propagation using PSO_Hill_A* and Genetic Algorithm

by Priyanka Sharma, Asha Mishra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 71 - Number 17
Year of Publication: 2013
Authors: Priyanka Sharma, Asha Mishra
10.5120/12453-9181

Priyanka Sharma, Asha Mishra . Optimizing Back-Propagation using PSO_Hill_A* and Genetic Algorithm. International Journal of Computer Applications. 71, 17 ( June 2013), 35-41. DOI=10.5120/12453-9181

@article{ 10.5120/12453-9181,
author = { Priyanka Sharma, Asha Mishra },
title = { Optimizing Back-Propagation using PSO_Hill_A* and Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 71 },
number = { 17 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 35-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume71/number17/12453-9181/ },
doi = { 10.5120/12453-9181 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:35:53.442032+05:30
%A Priyanka Sharma
%A Asha Mishra
%T Optimizing Back-Propagation using PSO_Hill_A* and Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 71
%N 17
%P 35-41
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Back propagation(BP) is used to solve real world problems which use the concept of multilayer perceptron(MLP). BP have the disadvantage of trapped in local minima, slow convergence rate and more error prone. To optimize BP Algorithm, Particle swarm optimization(PSO) and Genetic algorithm(GA) is used. Limitation of PSO_Hill and PSO_A* is overcomes when these algorithms are combined and on the basis of strength of these two algorithm we proposed a new PSO_Hill_A* algorithm which is used to optimize and enhance learning process in terms of convergence rate and accuracy. GA is a kind of method to simulate and to search the optimal solution, GA can have four operations including Encoding, selecting, crossover and Mutation. To optimize and improve BP, we proposed two architecture: 1) Use of PSO_Hill_A* before and after hidden layer. 2) Use of GA before and after hidden layer.

References
  1. Priyanka Sharma, Asha Mishra, "Optimizing Back-Propagation using PSO_Hill and PSO_A*", Int. J. of Scientific and Research Publications.
  2. H. Shayeghi, H. A. Shayanfar and G. Azimi, "Intelligent Neural Network Based STLF", Int. Journal of Intelligent Systems and Technologies, Vol. 4, No. 1, pp. 17-27, 2009.
  3. D. Srinivasan, "Evolving Artificial Neural Networks for Short Term Load Forecasting", Neurocomputing, Vol. 23, pp. 265-276, 1998.
  4. Z. A. Bashir and M. E. EL-Hawary, "Applying Wavelets to Short term Load Forecasting Using PSOBased Neural Networks", IEEE Trans. on Power Systems, Vol. 24, No. 1, pp. 20-27, 2009.
  5. C. F. Juang, "A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design, IEEE Trans. on systems, Man, and Cybernetics- Part B: Cybernetics, Vol. 34, No. 2, pp. 997-1006, 2004.
  6. H. Shayeghi, H. A. Shayanfar and G. Azimi, "STLF Based on Optimized Neural Network Using PSO", Int. J. of Electronics, Circuits and Systems, Vol. 3, No. 2, pp. 113-123, 2009.
  7. G. C. Liao and T. P. Tsao, "Application of a Fuzzy Neural Network Combined with a Chaos Genetic Algorithm and Simulated Annealing to Short term Load Forecasting", IEEE Trans. Evolutionary Computation, Vol. 10, No. 3, pp. 330-340, 2006.
  8. H. Shayeghi, H. A. Shayanfar, G. Azimi," A hybrid particle swarm optimization back propagation algorithm for short term load forecasting", Int. J . on Technical and Physical Problems of Engineering, Vol 2, Issue 4
  9. A. U. Asar, S. R. U. Hassnain and A. Khan, "Short term Load Forecasting Using Particle Swarm Optimization Based ANN Approach", Proc. of IEEE Int. Joint Conf. on Neural Networks, Orlando, Florida, USA, pp. 6, 2007.
  10. M. Clerc and J. Kennedy, "The Particle Swarm Explosion, Stability, and Convergence in a Multidimentional Complex Space", IEEE Trans. on Evolutionary Computation, Vol. 6, No. 1, pp. 58-73, 2002.
  11. J. R. Zhang, J. Zhang, T. M. Lok and M. R. Lyu, "A Hybrid Particle Swarm Optimization Back Propagation Algorithm for Feed-forward Neural Network Training", Applied Mathematics and Computation, Vol. 185, pp. 1026-1037, 2007.
  12. Y. Shi, "Particle Swarm Optimization", Electronic Data Systems, Inc. IEEE Neural Networks Society, pp. 8-13, 2004.
  13. Dr. Hanan A. R. Akkar, Samem Abass Salman," Training Artificial Neural Network Using Back-Propagation & Particle Swarm Optimization for Image Skin Diseases", Eng & Tech Journal, Vol. 29, No. 13, 2011.
  14. D. Graupe, "Principle of Artificial Neural Networks", World Scientific Publishing Co. Pte. Lte. , 2007.
  15. Darwin, Ch. (1859) The origin of species by means of natural selection.
  16. Whitley, D. (1994) A Genetic Algorithm Tutorial. Colorado State University, Computer Science Department.
  17. Holland, J. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press.
  18. Goldberg, D. (1987) Simple Genetic Algorithms and the Minimal, Deceptive Problem. In L. Davis, ed. , Pitman Genetic Algorithms And Simulated Annealing.
  19. J. Kennedy and R. Eberhart,"Particle swarm optimization",Proceedings of. IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.
  20. V. Selvi and Dr. R. Umarani, "Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques", International Journal of Computer Applications (0975 – 8887), Volume 5– No. 4, August 2010.
  21. Carlos Gershenson, "Artificial Neural Networks for Beginners"
  22. Matthew Conforth and Yan Meng ,"Reinforcement Learning for Neural Networks using Swarm Intelligence" , 2008 IEEE Swarm Intelligence Symposium, St. Louis MO USA, September 21-23, 2008
  23. Yi?it Karpat and Tu?rul Özel, "Swarm-Intelligent Neural Network System (SINNS) Based Multi- Objective Optimization Of Hard Turning", Transactions of NAMRI/SME Volume 34, 2006.
  24. James Kennedy' and Russell Eberhart2, "Particle Swarm Optimization", Washington, DC 20212,kennedy_jim@bls. gov,http://www. cs. tufts. edu/comp/150GA/homeworks/hw3/_reading6%201995%20particle%20swarming. pdf, 1995 IEEE.
  25. Sergi Perez, "Apply genetic algorithm to the learning phase of a neural network
Index Terms

Computer Science
Information Sciences

Keywords

PSO_Hill_A* BPA GA PSO_Hill PSO_A